Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.

IF 11.1 Q1 CELL BIOLOGY Cell genomics Pub Date : 2024-07-10 Epub Date: 2024-06-25 DOI:10.1016/j.xgen.2024.100592
Ziyang Zhang, Madeline E Melzer, Keerthana M Arun, Hanxiao Sun, Carl-Johan Eriksson, Itai Fabian, Sagi Shaashua, Karun Kiani, Yaara Oren, Yogesh Goyal
{"title":"Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.","authors":"Ziyang Zhang, Madeline E Melzer, Keerthana M Arun, Hanxiao Sun, Carl-Johan Eriksson, Itai Fabian, Sagi Shaashua, Karun Kiani, Yaara Oren, Yogesh Goyal","doi":"10.1016/j.xgen.2024.100592","DOIUrl":null,"url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, \"singletCode,\" to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100592"},"PeriodicalIF":11.1000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293576/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2024.100592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/25 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
合成 DNA 条形码识别 scRNA-seq 数据集中的单体并评估双体算法。
单细胞 RNA 测序(scRNA-seq)数据集除了包含真正的单细胞(或称单细胞)外,还包含在测序过程中聚合的细胞(或称双细胞)。在 scRNA-seq 中高保真地识别单细胞是避免假阴性和假阳性发现的必要条件。虽然已经提出了几种方法,但它们通常都是在高度异构的数据集上进行测试,缺乏对真正单体的先验知识。在这里,我们利用带有合成引入的 DNA 条形码的数据集进行了一项迄今为止尚未探索过的应用:提取地面真实单体。我们展示了我们的框架 "singletCode "的可行性,以评估各种情况下的现有双码检测方法。我们还利用我们的地面实况单点来训练一个概念验证机器学习分类器,该分类器的性能优于其他双重检测算法。我们的综合框架可以识别地面实况单字,并在非条码数据集中实现稳健的双字检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
7.10
自引率
0.00%
发文量
0
期刊最新文献
Contribution of germline and somatic mutations to risk of neuromyelitis optica spectrum disorder. Multiomic QTL mapping reveals phenotypic complexity of GWAS loci and prioritizes putative causal variants. Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia. Genetic mapping of serum metabolome to chronic diseases among Han Chinese. Single-cell DNA sequencing reveals pervasive positive selection throughout preleukemic evolution.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1